# Using Twitter Data to Understand Public Perceptions of Approved versus Off-label Use for COVID-19-related Medications arXiv: https://arxiv.org/abs/2206.14358 ยท HTML: https://mlsanity.com/paper/2206.14358/ ## Key facts - Cited by 8 papers in the map (data through 14 Oct 2026). - Builds on 16 of its 61 references that are in the map. - First posted on arXiv 29 Jun 2022. ## Builds on (most cited first, 16 of 16) - [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022) - [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020) - [TimeLMs: Diachronic Language Models from Twitter](https://mlsanity.com/paper/2202.03829/) (2022) - [Analyzing Polarization in Social Media: Method and Application to Tweets on 21 Mass Shootings](https://mlsanity.com/paper/1904.01596/) (2019) - [The COVID-19 Social Media Infodemic](https://mlsanity.com/paper/2003.05004/) (2020) - [Demographic Inference and Representative Population Estimates from Multilingual Social Media Data](https://mlsanity.com/paper/1905.05961/) (2019) - [Me, My Echo Chamber, and I: Introspection on Social Media Polarization](https://mlsanity.com/paper/1803.01731/) (2018) - [COVID-19 Vaccine Hesitancy on Social Media: Building a Public Twitter Dataset of Anti-vaccine Content, Vaccine Misinformation and Conspiracies](https://mlsanity.com/paper/2105.05134/) (2021) - [A Stance Data Set on Polarized Conversations on Twitter about the Efficacy of Hydroxychloroquine as a Treatment for COVID-19](https://mlsanity.com/paper/2009.01188/) (2020) - [Cross-language sentiment analysis of European Twitter messages duringthe COVID-19 pandemic](https://mlsanity.com/paper/2008.12172/) (2020) - [Annotating the Tweebank Corpus on Named Entity Recognition and Building NLP Models for Social Media Analysis](https://mlsanity.com/paper/2201.07281/) (2022) - [Analysis of the influence of political polarization in the vaccination stance: the Brazilian COVID-19 scenario](https://mlsanity.com/paper/2110.03382/) (2021) - [Face Off: Polarized Public Opinions on Personal Face Mask Usage during the COVID-19 Pandemic](https://mlsanity.com/paper/2011.00336/) (2020) - [Engaging Politically Diverse Audiences on Social Media](https://mlsanity.com/paper/2111.02646/) (2021) - [The Ivory Tower Lost: How College Students Respond Differently than the General Public to the COVID-19 Pandemic](https://mlsanity.com/paper/2004.09968/) (2020) - [Understanding Public Opinion on Using Hydroxychloroquine for COVID-19 Treatment via Social Media](https://mlsanity.com/paper/2201.00237/) (2022) ## Built on by (most cited first, 8 of 8) - [Large Language Models in Mental Health Care: a Scoping Review](https://mlsanity.com/paper/2401.02984/) (2024) - [METS-CoV: A Dataset of Medical Entity and Targeted Sentiment on COVID-19 Related Tweets](https://mlsanity.com/paper/2209.13773/) (2022) - [Guardrails for avoiding harmful medical product recommendations and off-label promotion in generative AI models](https://mlsanity.com/paper/2406.16455/) (2024) - [Patients Speak, AI Listens: LLM-based Analysis of Online Reviews Uncovers Key Drivers for Urgent Care Satisfaction](https://mlsanity.com/paper/2503.20981/) (2025) - [Crowdsourcing-Based Knowledge Graph Construction for Drug Side Effects Using Large Language Models with an Application on Semaglutide](https://mlsanity.com/paper/2504.04346/) (2025) - [Mpox Narrative on Instagram: A Labeled Multilingual Dataset of Instagram Posts on Mpox for Sentiment, Hate Speech, and Anxiety Analysis](https://mlsanity.com/paper/2409.05292/) (2024) - [Towards High-Fidelity Synthetic Multi-platform Social Media Datasets via Large Language Models](https://mlsanity.com/paper/2505.02858/) (2025) - [Geospatial AI, Dataverse Metadata, and the Study of Place-Based Government](https://mlsanity.com/paper/2609.11674/) (2026) ## Abstract (arXiv) Understanding public discourse on emergency use of unproven therapeutics is crucial for monitoring safe use and combating misinformation. We developed a natural language processing-based pipeline to comprehend public perceptions of and stances on coronavirus disease 2019 (COVID-19)-related drugs on Twitter over time. This retrospective study included 609,189 US-based tweets from January 29, 2020, to November 30, 2021, about four drugs that garnered significant public attention during the COVID-19 pandemic: (1) Hydroxychloroquine and Ivermectin, therapies with anecdotal evidence; and (2) Molnupiravir and Remdesivir, FDA-approved treatments for eligible patients. Time-trend analysis was employed to understand popularity trends and related events. Content and demographic analyses were conducted to explore potential rationales behind people's stances on each drug. Time-trend analysis indicated that Hydroxychloroquine and Ivermectin were discussed more than Molnupiravir and Remdesivir, particularly during COVID-19 surges. Hydroxychloroquine and Ivermectin discussions were highly politicized, related to conspiracy theories, hearsay, and celebrity influences. The distribution of stances between the two major US political parties was significantly different (P < .001); Republicans were more likely to support Hydroxychloroquine (55%) and Ivermectin (30%) than Democrats. People with healthcare backgrounds tended to oppose Hydroxychloroquine (7%) more than the general population, while the general population was more likely to support Ivermectin (14%). Our study found that social media users have varying perceptions and stances on off-label versus FDA-authorized drug use at different stages of COVID-19. This indicates that health systems, regulatory agencies, and policymakers should design tailored strategies to monitor and reduce misinformation to promote safe drug use.